Collapse duplicate categorical values within a row (#125)

- Closes #120

R/process.R

@@ -190,7 +190,7 @@ dataset_process <- function(filename_data_raw,
 
   # Record methods
   methods <- process_format_methods(metadata, dataset_id, sources, contributors)
-  
+
   # Retrieve taxonomic details for known species
   taxonomic_updates <-
     traits %>%
@@ -204,7 +204,7 @@ dataset_process <- function(filename_data_raw,
 
   # Taxon names explicitly excluded in metadata also excluded from taxonomic updates table.
   if (!is.na(metadata[["exclude_observations"]][1])) {
-    taxa_to_exclude <- 
+    taxa_to_exclude <-
         metadata[["exclude_observations"]] %>%
         traits.build::util_list_to_df2() %>%
         dplyr::mutate(
@@ -213,7 +213,7 @@ dataset_process <- function(filename_data_raw,
         tidyr::unnest_longer(.data$find) %>%
         dplyr::filter(.data$variable == "taxon_name")
 
-    taxonomic_updates <- 
+    taxonomic_updates <-
       taxonomic_updates %>%
       dplyr::filter(!.data$aligned_name %in% taxa_to_exclude$find)
   }
@@ -1863,10 +1863,10 @@ build_combine <- function(..., d = list(...)) {
 build_update_taxonomy <- function(austraits_raw, taxa) {
 
   columns_in_taxon_list <- names(taxa)
- 
+
   # incoming table from austraits_raw is a list of all taxa for the study
-  # `original_name` and `aligned_name` will be different if 
-  # there were taxonomic_updates specified in metadata file  
+  # `original_name` and `aligned_name` will be different if
+  # there were taxonomic_updates specified in metadata file
   austraits_raw$taxonomic_updates <-
     austraits_raw$taxonomic_updates %>%
     dplyr::left_join(
@@ -1896,7 +1896,7 @@ build_update_taxonomy <- function(austraits_raw, taxa) {
     dplyr::distinct() %>%
     util_df_convert_character() %>%
     dplyr::mutate(
-      # If no taxonomic resolution is specified from taxonomic_updates, 
+      # If no taxonomic resolution is specified from taxonomic_updates,
       # then the name's taxonomic resolution is the taxon_rank for the taxon name.
       taxonomic_resolution = ifelse(
         .data$taxon_name %in% taxa$aligned_name,
@@ -1907,7 +1907,7 @@ build_update_taxonomy <- function(austraits_raw, taxa) {
       # Create variable `name_to_match_to` which specifies the part of the taxon name to which matches can be made.
       # This step requires taxon_rank.
       name_to_match_to = stringr::str_replace(.data$taxon_name, " \\[.+",""),
-      name_to_match_to = ifelse(!.data$taxon_rank %in% c("species", "subspecies", "series", "variety", "form"), 
+      name_to_match_to = ifelse(!.data$taxon_rank %in% c("species", "subspecies", "series", "variety", "form"),
                                 stringr::word(.data$taxon_name,1), .data$name_to_match_to)
     ) %>%
     # Remove taxon_rank, as it is about to be merged back in, but matches will now be possible to more rows.

R/utils.R

@@ -102,9 +102,10 @@ util_separate_and_sort <- function(x, sep = " ") {
   # For those cells, split, sort then combine
   x[i] <- x[i] %>%
       stringr::str_split(" ") %>%
-      lapply(function(xi) xi %>% sort() %>% paste(collapse = " ")) %>%
+      lapply(function(xi) xi %>% sort() %>% unique() %>% paste(collapse = " ")) %>%
       unlist()
   x
+
 }
 
 #'  Convert dataframe to list

tests/testthat/examples/Test_2023_1/output/traits.csv

@@ -80,7 +80,7 @@ Test_2023_1,Alphitonia petriei,034,leaf_mass_per_area,149,g/m2,population,mean,m
 Test_2023_1,Alphitonia petriei,034,seed_dry_mass,27.2,mg,population,mean,measurement,3,field,adult,05,,,02,,01,14,03,02,2004/2004,from Falster_2005_1,01,,Alphitonia petriei
 Test_2023_1,Alphitonia petriei,034,seed_dry_mass,3--4,mg,population,bin,measurement,10,field,adult,05,,,02,,01,14,03,02,2004/2004,from Falster_2005_1,02,,Alphitonia petriei
 Test_2023_1,Alphitonia petriei,034,wood_density,0.413,mg/mm3,population,mean,measurement,3,field,adult,05,,,02,,01,14,03,02,2004/2004,from Falster_2005_1,01,,Alphitonia petriei
-Test_2023_1,Alphitonia petriei,035,plant_growth_form,mallee mallee shrub tree tree,,species,mode,expert_score,3,field,adult,,,,02,,,14,03,02,2004/2004,from Falster_2005_1,01,,Alphitonia petriei
+Test_2023_1,Alphitonia petriei,035,plant_growth_form,mallee shrub tree,,species,mode,expert_score,3,field,adult,,,,02,,,14,03,02,2004/2004,from Falster_2005_1,01,,Alphitonia petriei
 Test_2023_1,Alstonia scholaris,037,huber_value,0.000254323499491353,mm2{sapwood}/mm2{leaf},individual,mean,model_derived,5,field,adult,04,02,,,,02,11,,,2002/2002,made-up measurement remarks,05,,Alstonia scholaris
 Test_2023_1,Alstonia scholaris,038,leaf_length,40,mm,individual,mean,measurement,3,field,adult,04,02,,,,02,11,,,2004/2004,from Falster_2005_1,01,,Alstonia scholaris
 Test_2023_1,Alstonia scholaris,039,huber_value,0.000254323499491353,mm2{sapwood}/mm2{leaf},individual,raw,model_derived,5,field,adult,04,02,,,,02,11,,,,made-up measurement remarks,04,01,Alstonia scholaris